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Machine Learning for Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 48729

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Guest Editor
Russian Academy of Sciences, Melentiev Energy Systems Institute, 664033 Irkutsk, Russia
Interests: inverse problems; integral equations; machine learning; nonlinear systems; bifurcation; numerical methods; energy systems engineering; optimal design and operation; forecasting; energy storage systems
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Special Issue Information

Dear Colleagues,

We are inviting submissions to the Energies Special Issue on “Machine Learning for Energy Systems”.

Future energy systems will grow in complexity, causing both higher demands in reliability and increase in the degrees of freedom for functional improvement of integrated energy systems. Progress in heterogeneous data acquisition, data fusion, and mathematical modeling opens new perspectives in modern energy systems rethinking and improvement. This Special Issue of Energies aims at addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. Special attention will be given to the efficient mathematical methods integrating data-driven black box dynamical models with classical mathematical and mechanical models and methods.

Original submissions focusing on theoretical and practical issues related to machine learning method theory and applications, including novel optimization and operations research methods and their applications, design techniques and methodologies, reliability analysis, and practical implementation aspects are welcome.

The issue will include but is not be limited to:

  • Data-driven energy management strategies and unit commitment problem solvers;
  • Multiphysics measurements-based decision making and control of integrated energy systems;
  • Energy systems flexibility, efficiency and power quality;
  • Uncertainty quantification and inverse problems in energy systems.

Prof. Dr. Denis N. Sidorov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization
  • classification
  • optimal design and operation
  • energy systems
  • forecasting
  • multiple criteria decision-making
  • uncertainty in design and operation
  • operations research
  • inverse problems
  • clustering

Published Papers (15 papers)

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Editorial

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6 pages, 181 KiB  
Editorial
Machine Learning for Energy Systems
by Denis Sidorov, Fang Liu and Yonghui Sun
Energies 2020, 13(18), 4708; https://doi.org/10.3390/en13184708 - 10 Sep 2020
Cited by 10 | Viewed by 2561
Abstract
The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas [...] Read more.
The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)

Research

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19 pages, 6875 KiB  
Article
Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree
by Chin-Tan Lee and Shih-Cheng Horng
Energies 2020, 13(10), 2546; https://doi.org/10.3390/en13102546 - 17 May 2020
Cited by 8 | Viewed by 2521
Abstract
Failures of cast-resin transformers not only reduce the reliability of power systems, but also have great effects on power quality. Partial discharges (PD) occurring in epoxy resin insulators of high-voltage electrical equipment will result in harmful effects on insulation and can cause power [...] Read more.
Failures of cast-resin transformers not only reduce the reliability of power systems, but also have great effects on power quality. Partial discharges (PD) occurring in epoxy resin insulators of high-voltage electrical equipment will result in harmful effects on insulation and can cause power system blackouts. Pattern recognition of PD is a useful tool for improving the reliability of high-voltage electrical equipment. In this work, a fuzzy logic clustering decision tree (FLCDT) is proposed to diagnose the PD concerning the abnormal defects of cast-resin transformers. The FLCDT integrates a hierarchical clustering scheme with the decision tree. The hierarchical clustering scheme uses splitting attributes to divide the data set into suspended clusters according to separation matrices. The hierarchical clustering scheme is regarded as a preprocessing stage for classification using a decision tree. The whole data set is divided by the hierarchical clustering scheme into some suspended clusters, and the patterns in each suspended cluster are classified by the decision tree. The FLCDT was successfully adopted to classify the aberrant PD of cast-resin transformers. Classification results of FLCDT were compared with two software packages, See5 and CART. The FLCDT performed much better than the CART and See5 in terms of classification precisions. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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15 pages, 5428 KiB  
Article
A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism
by Ruixuan Yang, Fulin Zhou and Kai Zhong
Energies 2020, 13(8), 1904; https://doi.org/10.3390/en13081904 - 13 Apr 2020
Cited by 6 | Viewed by 2136
Abstract
In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power [...] Read more.
In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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11 pages, 1198 KiB  
Article
Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction
by Ahmad Nayyar Hassan and Ayman El-Hag
Energies 2020, 13(7), 1735; https://doi.org/10.3390/en13071735 - 5 Apr 2020
Cited by 18 | Viewed by 2768
Abstract
This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization [...] Read more.
This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization of the model, the training data was obtained from one utility company and the testing data was obtained from another utility. The model results in an optimal accuracy of 0.87 and a F1-score of 0.89. Detailed studies were also carried out to find the conditions under which the model renders optimal results. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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20 pages, 3050 KiB  
Article
An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm
by Rongyong Zhao, Daheng Dong, Cuiling Li, Steven Liu, Hao Zhang, Miyuan Li and Wenzhong Shen
Energies 2020, 13(7), 1549; https://doi.org/10.3390/en13071549 - 26 Mar 2020
Cited by 7 | Viewed by 3169
Abstract
Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine [...] Read more.
Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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18 pages, 5140 KiB  
Article
Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region
by Denis Sidorov, Daniil Panasetsky, Nikita Tomin, Dmitriy Karamov, Aleksei Zhukov, Ildar Muftahov, Aliona Dreglea, Fang Liu and Yong Li
Energies 2020, 13(5), 1226; https://doi.org/10.3390/en13051226 - 6 Mar 2020
Cited by 32 | Viewed by 3663
Abstract
Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems [...] Read more.
Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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16 pages, 3505 KiB  
Article
Operational Risk Assessment of Electric-Gas Integrated Energy Systems Considering N-1 Accidents
by Hua Liu, Yong Li, Yijia Cao, Zilong Zeng and Denis Sidorov
Energies 2020, 13(5), 1208; https://doi.org/10.3390/en13051208 - 5 Mar 2020
Cited by 11 | Viewed by 2637
Abstract
The reliability analysis method and risk assessment model for the traditional single network no longer meet the requirements of the risk analysis of coupled systems. This paper establishes a risk assessment system of electric-gas integrated energy system (EGIES) considering the risk security of [...] Read more.
The reliability analysis method and risk assessment model for the traditional single network no longer meet the requirements of the risk analysis of coupled systems. This paper establishes a risk assessment system of electric-gas integrated energy system (EGIES) considering the risk security of components. According to the mathematical model of each component, the EGIES steady state analysis model considering the operation constraints is established to analyze the operation status of each component. Then the EGIES component accident set is established to simulate the accident consequences caused by the failure of each component to EGIES. Furthermore, EGIES risk assessment system is constructed to identify the vulnerability of EGIES components. Finally, the risk assessment of IEEE14-NG15 system is carried out. The simulation results verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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17 pages, 1785 KiB  
Article
Cluster-Based Prediction for Batteries in Data Centers
by Syed Naeem Haider, Qianchuan Zhao and Xueliang Li
Energies 2020, 13(5), 1085; https://doi.org/10.3390/en13051085 - 1 Mar 2020
Cited by 3 | Viewed by 3413
Abstract
Prediction of a battery’s health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery’s degradation status is very critical, even before the first failure is [...] Read more.
Prediction of a battery’s health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery’s degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery’s health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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19 pages, 5123 KiB  
Article
Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle
by Fulin Zhou, Feifan Liu, Ruixuan Yang and Huanrui Liu
Energies 2020, 13(4), 879; https://doi.org/10.3390/en13040879 - 17 Feb 2020
Cited by 9 | Viewed by 2220
Abstract
The excessive use of power electronics makes power quality problems in power grids increasingly prominent. The estimation of the harmonic parameters of harmonic sources in the power grid and the division of harmonic responsibilities are of great significance for the evaluation of power [...] Read more.
The excessive use of power electronics makes power quality problems in power grids increasingly prominent. The estimation of the harmonic parameters of harmonic sources in the power grid and the division of harmonic responsibilities are of great significance for the evaluation of power quality. At present, methods for estimating harmonic parameters and harmonic responsibilities need to provide the amplitude and phase information of the current and voltage of the point of common coupling (PCC). However, in practical engineering applications, the general power quality monitor only provides the amplitude information of the voltage and current of the measured point and the phase difference information between them. Missing phase information invalidates existing methods. Based on the partial least squares regression method, the present work proposes a method for estimating harmonic parameters in the case of monitoring data without phase. This method only needs to measure the amplitude information of the harmonic voltage and current of the PCC and the phase difference between them, then use the measurable data to estimate the harmonic parameters and the harmonic responsibility of each harmonic source. It provides a new way to effectively solve the problem that the measured data of the project has no phase information. The feasibility and effectiveness of the proposed method are proved by simulation data and measured engineering data. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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19 pages, 2725 KiB  
Article
Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System
by Stéfano Frizzo Stefenon, Roberto Zanetti Freire, Leandro dos Santos Coelho, Luiz Henrique Meyer, Rafael Bartnik Grebogi, William Gouvêa Buratto and Ademir Nied
Energies 2020, 13(2), 484; https://doi.org/10.3390/en13020484 - 19 Jan 2020
Cited by 54 | Viewed by 3851
Abstract
The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain [...] Read more.
The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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22 pages, 5214 KiB  
Article
Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture
by Gangjun Gong, Zhening Zhang, Xinyu Zhang, Nawaraj Kumar Mahato, Lin Liu, Chang Su and Haixia Yang
Energies 2020, 13(2), 418; https://doi.org/10.3390/en13020418 - 15 Jan 2020
Cited by 14 | Viewed by 3076
Abstract
With the integration of highly permeable renewable energy to the grid at different levels (transmission, distribution and grid-connected), the volatility on both sides (source side and load side) leading to bidirectional power flow in the power grid complicates the control mechanism. In order [...] Read more.
With the integration of highly permeable renewable energy to the grid at different levels (transmission, distribution and grid-connected), the volatility on both sides (source side and load side) leading to bidirectional power flow in the power grid complicates the control mechanism. In order to ensure the real-time power balance, energy exchange, higher energy utilization efficiency and stability maintenance in the electric power system, this paper proposes an integrated application of blockchain technology on energy routers at transmission and distribution networks with increased renewable energy penetration. This paper focuses on the safe and stable operation of a highly penetrated renewable energy grid-connected power system and its operation. It also demonstrates a blockchain-based negotiation model with weakly centralized scenarios for “source-network-load” collaborative scheduling operations; secondly, the QoS (quality of service) index of energy flow control and energy router node doubly-fed stability control model were designed. Further, it also introduces the MOPSO (multi-objective particle swarm optimization) algorithm for power output optimization of multienergy power generation; Thirdly, based on the blockchain underlying architecture and load prediction value constraints, this paper puts forward the optimization mechanism and control flow of autonomous energy coordination of b2u (bottom-up) between router nodes of transmission and distribution network based on blockchain. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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17 pages, 3761 KiB  
Article
A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM
by Sen Wang, Yonghui Sun, Yan Zhou, Rabea Jamil Mahfoud and Dongchen Hou
Energies 2020, 13(1), 87; https://doi.org/10.3390/en13010087 - 23 Dec 2019
Cited by 29 | Viewed by 2507
Abstract
The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and [...] Read more.
The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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16 pages, 3499 KiB  
Article
Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model
by Fang Liu, Ranran Li and Aliona Dreglea
Energies 2019, 12(18), 3551; https://doi.org/10.3390/en12183551 - 17 Sep 2019
Cited by 35 | Viewed by 2904
Abstract
Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on [...] Read more.
Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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15 pages, 2611 KiB  
Article
An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making
by Senhui Wang, Haifeng Li, Yongjie Zhang and Zongshu Zou
Energies 2019, 12(18), 3535; https://doi.org/10.3390/en12183535 - 15 Sep 2019
Cited by 11 | Viewed by 2672
Abstract
The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher [...] Read more.
The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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Review

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24 pages, 5902 KiB  
Review
Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application
by Zilong Zeng, Yong Li, Yijia Cao, Yirui Zhao, Junjie Zhong, Denis Sidorov and Xiangcheng Zeng
Energies 2020, 13(4), 881; https://doi.org/10.3390/en13040881 - 17 Feb 2020
Cited by 44 | Viewed by 6152
Abstract
In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network [...] Read more.
In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network construction, and the latter is susceptible to interference and attacks in the process of information dissemination. The blockchain is an emerging decentralized architecture and distributed computing paradigm. Under the premise that these nodes do not need mutual trust, the blockchain can implement trusted peer-to-peer communication for protecting the important information by adopting distributed consensus mechanisms, encryption algorithms, point-to-point transmission and smart contracts. In response to the above issues, this paper firstly analyzes the information security problems existing in the energy internet from the four perspectives of system control layer, device access, market transaction and user privacy. Then blockchain technology is introduced, and its working principles and technical characteristics are analyzed. Based on the technical characteristics, we propose the multilevel and multichain information transmission model for the weak centralization of scheduling and the decentralization of transaction. Furthermore, we discuss that the information transmission model helps solve some of the information security issues from the four perspectives of system control, device access, market transaction and user privacy. Application examples are used to illustrate the technical features that benefited from the blockchain for the information security of the energy internet. Full article
(This article belongs to the Special Issue Machine Learning for Energy Systems)
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